Exploring the Performance of Multifactor Dimensionality Reduction in Large Scale SNP Studies and in the Presence of Genetic Heterogeneity among Epistatic Disease Models

Background/Aims: In genetic studies of complex disease a consideration for the investigator is detection of joint effects. The Multifactor Dimensionality Reduction (MDR) algorithm searches for these effects with an exhaustive approach. Previously unknown aspects of MDR performance were the power to detect interactive effects given large numbers of non-model loci or varying degrees of heterogeneity among multiple epistatic disease models. Methods: To address the performance with many non-model loci, datasets of 500 cases and 500 controls with 100 to 10,000 SNPs were simulated for two-locus models, and one hundred 500-case/500-control datasets with 100 and 500 SNPs were simulated for three-locus models. Multiple levels of locus heterogeneity were simulated in several sample sizes. Results: These results show MDR is robust to locus heterogeneity when the definition of power is not as conservative as in previous simulation studies where all model loci were required to be found by the method. The results also indicate that MDR performance is related more strongly to broad-sense heritability than sample size and is not greatly affected by non-model loci. Conclusions: A study in which a population with high heritability estimates is sampled predisposes the MDR study to success more than a larger ascertainment in a population with smaller estimates.

[1]  David M. Reif,et al.  A comparison of analytical methods for genetic association studies , 2008, Genetic epidemiology.

[2]  Marylyn D. Ritchie,et al.  Alternative contingency table measures improve the power and detection of multifactor dimensionality reduction , 2008, BMC Bioinformatics.

[3]  Todd L Edwards,et al.  Genetic heterogeneity is not as threatening as you might think , 2007, Genetic epidemiology.

[4]  Jun Zhu,et al.  A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence. , 2007, American journal of human genetics.

[5]  Scott M. Williams,et al.  A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction , 2007, Genetic epidemiology.

[6]  Alison A Motsinger,et al.  The effect of reduction in cross‐validation intervals on the performance of multifactor dimensionality reduction , 2006, Genetic epidemiology.

[7]  Todd Holden,et al.  A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. , 2006, Journal of theoretical biology.

[8]  J L Haines,et al.  Multifactor dimensionality reduction reveals gene–gene interactions associated with multiple sclerosis susceptibility in African Americans , 2006, Genes and Immunity.

[9]  E R Martin,et al.  An Analysis Paradigm for Investigating Multi‐locus Effects in Complex Disease: Examination of Three GABAA Receptor Subunit Genes on 15q11‐q13 as Risk Factors for Autistic Disorder. , 2006, Annals of human genetics.

[10]  Margaret R Karagas,et al.  Concordance of multiple analytical approaches demonstrates a complex relationship between DNA repair gene SNPs, smoking and bladder cancer susceptibility. , 2006, Carcinogenesis.

[11]  Scott M. Williams,et al.  Single-nucleotide polymorphisms for diagnosis of salt-sensitive hypertension. , 2006, Clinical chemistry.

[12]  Per Eriksson,et al.  Epistatic and pleiotropic effects of polymorphisms in the fibrinogen and coagulation factor XIII genes on plasma fibrinogen concentration, fibrin gel structure and risk of myocardial infarction , 2006, Thrombosis and Haemostasis.

[13]  J. H. Moore,et al.  A novel method to identify gene–gene effects in nuclear families: the MDR‐PDT , 2006, Genetic epidemiology.

[14]  Marylyn D. Ritchie,et al.  Data Simulation Software for Whole-Genome Association and Other Studies in Human Genetics , 2005, Pacific Symposium on Biocomputing.

[15]  Jason H. Moore,et al.  The Interaction of Four Genes in the Inflammation Pathway Significantly Predicts Prostate Cancer Risk , 2005, Cancer Epidemiology Biomarkers & Prevention.

[16]  E R Martin,et al.  Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. , 2005, American journal of human genetics.

[17]  Lin He,et al.  An association study of the N-methyl-D-aspartate receptor NR1 subunit gene (GRIN1) and NR2B subunit gene (GRIN2B) in schizophrenia with universal DNA microarray , 2005, European Journal of Human Genetics.

[18]  Scott M. Williams,et al.  Traversing the conceptual divide between biological and statistical epistasis: systems biology and a more modern synthesis. , 2005, BioEssays : news and reviews in molecular, cellular and developmental biology.

[19]  Serge Batalov,et al.  Susceptibility and modifier genes in Portuguese transthyretin V30M amyloid polyneuropathy: complexity in a single-gene disease. , 2005, Human molecular genetics.

[20]  Jason H Moore,et al.  Computational analysis of gene-gene interactions using multifactor dimensionality reduction , 2004, Expert review of molecular diagnostics.

[21]  Marylyn D Ritchie,et al.  Renin-Angiotensin System Gene Polymorphisms and Atrial Fibrillation , 2004, Circulation.

[22]  D. Nyholt.  A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. , 2004, American journal of human genetics.

[23]  Marylyn D. Ritchie,et al.  Multilocus Analysis of Hypertension: A Hierarchical Approach , 2004, Human Heredity.

[24]  J. H. Moore,et al.  Multifactor-dimensionality reduction shows a two-locus interaction associated with Type 2 diabetes mellitus , 2004, Diabetologia.

[25]  Chiara Sabatti,et al.  False discovery rate in linkage and association genome screens for complex disorders. , 2003, Genetics.

[26]  Jason H. Moore,et al.  Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions , 2003, Bioinform..

[27]  Jason H. Moore,et al.  Power of multifactor dimensionality reduction for detecting gene‐gene interactions in the presence of genotyping error, missing data, phenocopy, and genetic heterogeneity , 2003, Genetic epidemiology.

[28]  Joseph D Terwilliger,et al.  Confounding, ascertainment bias, and the blind quest for a genetic ‘fountain of youth’ , 2003, Annals of medicine.

[29]  T. Reich,et al.  A perspective on epistasis: limits of models displaying no main effect. , 2002, American journal of human genetics.

[30]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[31]  G. Mendel,et al.  Mendel's Principles of Heredity , 1910, Nature.

[32]  G. Wong,et al.  Gene-gene interactions for asthma and plasma total IgE concentration in Chinese children. , 2006, The Journal of allergy and clinical immunology.

[33]  David V Conti,et al.  A testing framework for identifying susceptibility genes in the presence of epistasis. , 2006, American journal of human genetics.

[34]  Jason H. Moore,et al.  Ideal discrimination of discrete clinical endpoints using multilocus genotypes , 2004, Silico Biol..

[35]  Harlan M Krumholz,et al.  Reporting of model validation procedures in human studies of genetic interactions. , 2004, Nutrition.

[36]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[37]  R. Fisher.  XV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance. , 1919, Transactions of the Royal Society of Edinburgh.

[38]  L. Penrose,et al.  THE CORRELATION BETWEEN RELATIVES ON THE SUPPOSITION OF MENDELIAN INHERITANCE , 2022 .